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Sunduz Keles, Ph.D.
Professor
Department of Biostatistics & Medical Informatics and Department of Statistics
University of Wisconsin

Hi-C assays are currently state-of-the-art for mapping long-range chromatin interactions genome-wide and data from these assays are now being routinely used to interpret results from genome-wide association studies.   I will present two methods that we have developed for addressing critical needs in the analysis of Hi-C Data: mHi-C for leveraging multi-mapping reads and FreeHi-C for simulating realistic Hi-C data for benchmarking and data augmentation. In the second part of the talk, I will introduce atSNP Search which provides statistical evaluation of impact of SNPs on transcription factor-DNA interactions (atSNP Search: http://atsnp.biostat.wisc.edu/).